Margarita Sanromán-Junquera

Implantable Defibrillator Electrograms and Origin of Left Ventricular Impulses: An Analysis of Regionalization Ability and Visual Spatial Resolution

ICD Electrograms and Origin of Impulses. Introduction: The implantable cardioverter‐defibrillator (ICD) electrogram (EG) is a documentation of ventricular tachycardia. We prospectively analyzed EGs from ICD electrodes located at the right ventricle apex to establish (1) ability to regionalize origin of left ventricle (LV) impulses, and (2) spatial resolution to distinguish between paced sites. Methods and Results: LV electro‐anatomic maps were generated in 15 patients. ICD‐EGs were recorded during pacing from 22 ± 10 LV sites. Voltage of far‐field EG deflections (initial, peak, final) and time intervals between far‐field and bipolar EGs were measured. Blinded visual analysis was used for spatial resolution. Initial deflections were more negative and initial/peak ratios were larger for lateral versus septal and superior versus inferior sites. Time intervals were shorter for apical versus basal and septal versus lateral sites. Best predictive cutoff values were voltage of initial deflection <–1.24 mV, and initial/peak ratio >0.45 for a lateral site, voltage of final deflection <–0.30 for an inferior site, and time interval <80 milliseconds for an apical site. In a subsequent group of 9 patients, these values predicted correctly paced site location in 54–75% and tachycardia exit site in 60–100%. Recognition of paced sites as different by EG inspection was 91% accurate. Sensitivity increased with distance (0.96 if ≥ 2 cm vs 0.84 if < 2 cm, P  <  0.001) and with presence of low‐voltage tissue between sites (0.94 vs 0.88, P  <  0.001). Conclusions: Standard ICD‐EG analysis can help regionalize LV sites of impulse formation. It can accurately distinguish between 2 sites of impulse formation if they are ≥2 cm apart. (J Cardiovasc Electrophysiol, Vol. 23, pp. 506‐514, May 2012)

Energy Efficiency and Quality of Data Reconstruction Through Data-Coupled Clustering for Self-Organized Large-Scale WSNs

Energy efficiency has been a leading issue in Wireless Sensor Networks (WSNs) and has produced a vast amount of research. Although the classic tradeoff has been between the quality of gathered data versus the lifetime of the network, most works gave preference to an increased network lifetime at the expense of the data quality. A common approach for energy efficiency is partitioning the network into clusters with correlated data, where the representative nodes simply transmit or average measurements inside the cluster. In this paper, we explore the joint use of in-network processing techniques and clustering algorithms. This approach seeks both high data quality with a controlled number of transmissions using an aggregation function and an energy efficient network partition, respectively. The aim of this combination is to increase energy efficiency without sacrificing the data quality. We compare the performance of the Second-Order Data-Coupled Clustering (SODCC) and Compressive-Projections Principal Component Analysis (CPPCA) algorithm combination, in terms of both the energy consumption and the quality of the data reconstruction, to other combinations of the state-of-the-art clustering algorithms and in-network processing techniques. Among all the considered cases, the SODCC + CPPCA combination revealed a perfect balance between data quality, energy expenditure, and ease of network management. The main conclusion of this paper is that the design of WSN algorithms must be processing-oriented rather than transmission-oriented, i.e., investing energy on both the clustering and in-network processing algorithms ensures both energy efficiency and data quality.

Digital recovery of biomedical signals from binary images

Given the vast amount of historical clinical data to be incorporated from old hospital information systems into new emerging digital storing standards, digital recovery of paper-written one-dimensional biomedical signals is a relevant application. Signal recovery from noisy, black and white, grid paper printout recordings, is a real situation that has received little attention in the literature. In this paper we propose an integral, automatic approach, based on digital image processing principles, and implemented in four stages: (1) orientation correction of the scanned image, using the eigenvector decomposition of the foreground pixel coordinates, hence reducing the computational cost of subsequent Hough Transform; (2) grid detection, using the Discrete Cosine Transform on horizontal and vertical histogram projections; (3) signal waveform identification, using morphological operators; (4) conversion from the waveform in the image plane to the one-dimensional biomedical signal. Time synchronization between the digitized gold standard and the recovered signals, which is essential for performance evaluation, is addressed by using of contrast filters to extract fiducial points on both signals, which are then fitted to a regression curve. Results with black and white paper printout recordings of intracardiac signals show that proposed approach is capable of automatically recovering biomedical signals from noisy images.

Arrhythmia Mechanism and Scaling Effect on the Spectral Properties of Electroanatomical Maps With Manifold Harmonics

Introduction: Spatial and temporal processing of intracardiac electrograms provides relevant information to support the arrhythmia ablation during electrophysiological studies. Current cardiac navigation systems (CNS) and electrocardiographic imaging (ECGI) build detailed 3-D electroanatomical maps (EAM), which represent the spatial anatomical distribution of bioelectrical features, such as activation time or voltage. Objective: We present a principled methodology for spectral analysis of both EAM geometry and bioelectrical feature in CNS or ECGI, including their spectral representation, cutoff frequency, or spatial sampling rate (SSR). Methods: Existing manifold harmonic techniques for spectral mesh analysis are adapted to account for a fourth dimension, corresponding to the EAM bioelectrical feature. Appropriate scaling is required to address different magnitudes and units. Results: With our approach, simulated and real EAM showed strong SSR dependence on both the arrhythmia mechanism and the cardiac anatomical shape. For instance, high frequencies increased significantly the SSR because of the “early-meets-late” in flutter EAM, compared with the sinus rhythm. Besides, higher frequency components were obtained for the left atrium (more complex anatomy) than for the right atrium in sinus rhythm. Conclusion: The proposed manifold harmonics methodology opens the field toward new signal processing tools for principled EAM spatiofeature analysis in CNS and ECGI, and to an improved knowledge on arrhythmia mechanisms.

A new approach to the intracardiac inverse problem using Laplacian distance kernel

BackgroundThe inverse problem in electrophysiology consists of the accurate estimation of the intracardiac electrical sources from a reduced set of electrodes at short distances and from outside the heart. This estimation can provide an image with relevant knowledge on arrhythmia mechanisms for the clinical practice. Methods based on truncated singular value decomposition (TSVD) and regularized least squares require a matrix inversion, which limits their resolution due to the unavoidable low-pass filter effect of the Tikhonov regularization techniques.MethodsWe propose to use, for the first time, a Mercer’s kernel given by the Laplacian of the distance in the quasielectrostatic field equations, hence providing a Support Vector Regression (SVR) formulation by following the principles of the Dual Signal Model (DSM) principles for creating kernel algorithms.ResultsSimulations in one- and two-dimensional models show the performance of our Laplacian distance kernel technique versus several conventional methods. Firstly, the one-dimensional model is adjusted for yielding recorded electrograms, similar to the ones that are usually observed in electrophysiological studies, and suitable strategy is designed for the free-parameter search. Secondly, simulations both in one- and two-dimensional models show larger noise sensitivity in the estimated transfer matrix than in the observation measurements, and DSM−SVR is shown to be more robust to noisy transfer matrix than TSVD.ConclusionThese results suggest that our proposed DSM−SVR with Laplacian distance kernel can be an efficient alternative to improve the resolution in current and emerging intracardiac imaging systems.

Influence of Normalization on the Analysis of Electroanatomical Maps with Manifold Harmonics

Electrical and anatomical maps (EAM) are built by cardiac navigation systems (CNS) and by Electrocardiographic Imaging systems for supporting arrhythmia ablation during electrophysiological procedures. Manifold Harmonics Analysis (MHA) has been proposed for analyzing the spectral properties of EAM of voltages and times in CNS by using a representation of the EAM supported by the anatomical mesh. MHA decomposes the EAM in a set of basis functions and coefficients which allow to conveniently reconstruct the EAM. In this work, we addressed the effect of normalization of the mesh spatial coordinates and the bioelectrical feature on the EAM decomposition for identifying regions with strong variation on the feature. For this purpose, a simulated EAM with three foci in a ventricular and in an atrial tachycardia was used. These foci were located at different distances amongst themselves, and different voltages were also considered. Our experiments show that it is possible to identify the foci origin by considering the first 3–5 projections only when normalization was considered, both for atrial and ventricular EAM. In this case, better quality in the EAM reconstruction was also obtained when using less basis functions. Hence, we conclude that normalization can help to identify regions with strong feature variation in the first stages of the EAM reconstruction.

A Support Vector Laplacian Distance Kernel Approach to the Inverse Problem in Intracardiac Electrophysiology

The accurate estimation of the intracardiac electrical sources from a reduced set of electrodes at some distance from the heart chamber is known as the inverse problem in electrophysiology, and it can provide with relevant knowledge on a number of arrhythmia mechanisms in the clinical practice. Methods based on Singular Value Decomposition and Least Squares require a matrix inversion and exhibit limited resolution, due to the low-pass filter effect of the Tikhonov regularization techniques. We propose to use a Dual Problem Signal Model formulation of the ν -Support Vector Regression (ν -SVR) algorithm, with a Mercer Kernel given by Laplacian of the distance function accounting for quasielectrostatic field conditions. This new approach avoids the matrix inversion while providing with high resolution and improved generalization properties. Simulations on simple one-dimensional synthetic examples show the performance in terms of improved resolution and boundary region detection. Also, the choice of the free parameters in the ν - SVR algorithm is related to several bioelectric properties of the problem. Results suggest that ν -SVR with a Laplacian distance kernel can be a suitable alternative for improved resolution in current and emerging non-contact cardiac imaging systems.

Spectral analysis of Electroanatomical Maps for spatial bandwidth estimation as support to ablation

Intracardiac electrograms (EGM) in Cardiac Navigation Systems (CNS) and in Electrocardiographic Imaging provide relevant information on the arrhythmia mechanism for supporting ablation. In our previous work, we proposed Manifold Harmonics Analysis (MHA) for establishing the spatial sampling rate in ElectroAnatomical Maps (EAM) accounting for anatomical and bioelectrical features (e.g., voltage or activation time). Here, we propose a theoretically founded method for spectrum representation in terms of spatial frequencies from MHA, which can determine the minimum number of EGM registered at different spatial positions for accurate EAM with a cut-off spatial bandwidth. The EAM spectrum magnitude is obtained by cross-correlation between the original spatial anatomical and bioelectrical features, and the corresponding coefficients projected onto the manifold harmonic basis. The cut-off spatial frequency is computed according to a threshold value (TH ∈ [0,1]), accounting for the EAM reconstruction quality. TH was scrutinized in high quality anatomical meshes from tomography images, and in simulated and real EAM from CNS. Experiments showed that TH>0.98 is required to obtain accurate both anatomical meshes and EAM. Strong dependence was shown on EAM with the cut-off spatial frequency in terms of the arrhythmia mechanism.

Effect of interpolation on electroanatomical mapping

Cardiac navigation systems (CNS) are often used in electrophysiological studies to create spatial-electrical maps supporting the arrhythmia mechanism identification. Sequentially recorded electrograms yield the bioelectrical information from features such as voltage and activation times in terms of their spatial location, which are subsequently interpolated for building the electroanatomical map (EAM) of the cardiac chamber. Our goal was to evaluate quantitatively the effect of interpolation in the EAM accuracy when reconstructed from a set of samples. Triangulated irregular networks (TIN), thin plate spline (TPS), and support vector machines (SVM) were assessed by using: (a) two detailed simulated time activation maps during flutter and sinus rhythm in both atria; (b) a set of real CNS maps, given by 13 activation time and 19 voltage maps, with 6 right atria (RA), 6 left atria (LA), 4 right ventricles (RV), and 16 left ventricles (LV). Interpolation methods were benchmarked using root mean squared error (RMSE), efficiency (EF), and Willmott distance (WD). On the one hand, EF and WD were similar for yielding a clearer cut-off point than RMSE for the number of required samples, which was about 100. Better EAM accuracy was obtained using TPS, followed by SVM and TIN, except for flutter in the RA, where early-meets-late was smoothed by SVM. On the other hand, EAM accuracy (in terms of the average WD) was slightly outperformed by RA than LA (0.57 vs 0.52), whereas RV and LV were similar (0.71 vs 0.71). In reference to the methods, similar average WD was given by the interpolation methods (TIN 0.64 ± 0.14; TPS 0.66 ± 0.15; SVM 0.65 ± 0.18). The EAM accuracy is dependent on the map nature and on the cardiac chamber.

Automatic Supporting System for Regionalization of Ventricular Tachycardia Exit Site in Implantable Defibrillators

Electrograms stored in Implantable Cardioverter Defibrillators (ICD-EGM) have been proven to convey useful information for roughly determining the anatomical location of the Left Ventricular Tachycardia exit site (LVTES). Our aim here was to evaluate the possibilities from a machine learning system intended to provide an estimation of the LVTES anatomical region with the use of ICD-EGM in the situation where 12-lead electrocardiogram of ventricular tachycardia are not available. Several machine learning techniques were specifically designed and benchmarked, both from classification (such as Neural Networks (NN), and Support Vector Machines (SVM)) and regression (Kernel Ridge Regression) problem statements. Classifiers were evaluated by using accuracy rates for LVTES identification in a controlled number of anatomical regions, and the regression approach quality was studied in terms of the spatial resolution. We analyzed the ICD-EGM of 23 patients (18±10 EGM per patient) during left ventricular pacing and simultaneous recording of the spatial coordinates of the pacing electrode with a navigation system. Several feature sets extracted from ICD-EGM (consisting of times and voltages) were shown to convey more discriminative information than the raw waveform. Among classifiers, the SVM performed slightly better than NN. In accordance with previous clinical works, the average spatial resolution for the LVTES was about 3 cm, as in our system, which allows it to support the faster determination of the LVTES in ablation procedures. The proposed approach also provides with a framework suitable for driving the design of improved performance future systems.